PROJECT SUMMARY Mobile technology has enormous potential for delivering highly innovative, dynamic smoking cessation interventions. Phone sensors, wearable technology, and real time data collection methods such as ecological momentary assessment (EMA) have made it possible to collect a wealth of environmental and physiological data such as location, heart rate, and mood. Environmental and situational cues such as craving and proximity to others smoking are highly predictive of lapse among those trying to quit, suggesting that lapse risk is characterized by immediate, dynamic influences. Emerging strategies such as just-in-time adaptive interventions (JITAI), aim to prevent smoking lapse using tailored support delivered via mobile technology in the moments when it is most needed. Although research has identified antecedents of smoking lapse based on observations from EMA data, studies have been unable to utilize the full spectrum of contextual and environmental data available with current technology. Given the importance of dynamic influences on lapse risk, there is a critical need for strategies that accurately identify moments of highest lapse risk to improve cessation interventions. Recent research has demonstrated the utility of machine learning to predict individual behavior. Machine learning is a robust data analytic strategy that can produce highly accurate predictive models from large datasets and can automatically adapt to new data in real time. The overall objective of this application is to use supervised machine learning methods to develop an automated algorithm to quantify smoking lapse risk at the individual level. Specifically, we aim: 1) to apply supervised machine learning methods to quantify personalized risk of smoking lapse, and 2) to evaluate the feasibility and preliminary effectiveness of delivering a personalized, just-in-time adaptive intervention driven by machine learning prediction of smoking lapse risk in real time. The proposed research and training plan will take place at The University of Oklahoma Health Sciences Center (OUHSC) and the Stephenson Cancer Center (SCC). Training will focus on increasing knowledge of machine learning methodology, and the conduct and analysis of JITAIs, which will facilitate completion of the proposed project. Results of the proposed research have the potential to reduce the amount and frequency of data needed from participants and sensors, enabling the development of less burdensome interventions. It is expected that completion of these aims will yield preliminary data to inform an automated, dynamic intervention that fully utilizes the strengths of mobile technology for measuring individual behavior and environmental context in real time.